Estimating physical parameters of a physical system based on a spatial-temporal emulator

ABSTRACT

Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers. The method further comprises training a spatial-temporal emulator model for a physical numerical model using the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, incorporating prior knowledge of the physical system into the spatial-temporal emulator model, estimating physical parameters of the physical system based on the spatial-temporal emulator model.

RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 16/550,234, filed Aug. 25, 2019, which claims priority to U.S.Patent Application Ser. No. 62/727,992, filed Sep. 6, 2018 and to U.S.Patent Application Ser. No. 62/728,000, filed Sep. 6, 2018, which areherein incorporated by reference.

FIELD OF THE DESCRIBED EMBODIMENTS

The described embodiments relate generally to physical systems. Moreparticularly, the described embodiments relate to systems, methods andapparatuses for estimating physical parameters of a physical systembased on a model of the spatial-temporal dynamics of physical processes.

BACKGROUND

The current paradigm in environmental computational fluid dynamics (CFD)is process-driven simulation: known laws encoded in systems of coupledpartial differential equations (PDEs) are solved over space and time vianumerical schemes. Generating a single hypothesis of thespatial-temporal evolution of a realistic system over a realistic domain(e.g., hourly wildfire spread or air pollution dynamics for a city) isextremely compute-intensive. Data assimilation in process based modelsis highly compute-intensive and incorporates only 2-5% of the volume ofever increasing remote-sensing observations. Moreover, realisticprocess-based models cannot be deployed on modern edge devices, becauseof severe memory, power consumption, and data transmission constraints.Thus, they cannot be used for in-the-field decision making with harshconditions that limit cloud connectivity (e.g., wildfire firstresponse).

Thus, it is desirable to have methods, apparatuses, and systems forestimating physical parameters of a physical system based on models thatemulate the complex spatial-temporal dynamics of numerical physicsmodels, yet employ highly efficient computational architectures toperform these estimations, deployable on a variety of computing devices.

SUMMARY

An embodiment includes a method for generating simulations of physicalvariables of a physical system. The method includes obtainingobservational data, wherein the observational data includes at least onesource of physical data, comprising one or more sensors sensing thephysical data, obtaining numeric simulation data, and fusing theobservation data and the numeric simulation data. The fusing includespreprocessing the observational data and the numeric simulation data toremove inconsistencies of the observational data and the numericsimulation data, processing the preprocessed observational data and thenumeric simulation data to extract interpretable structures and patternswithin that data using ground truth and labeled information to createdomain interpretable data, normalizing the preprocessed observationdata, the numeric simulation data, and the domain interpretable datalayers, and increasing resolution of a gridding of the normalizedpreprocessed observation data, numeric simulation data, and domaininterpretable data layers. The method further comprises training aspatial-temporal emulator model for a physical numerical model using thenormalized preprocessed observation data, the numeric simulation data,and the domain interpretable data, incorporating prior knowledge of thephysical system into the spatial-temporal emulator model, estimating oneor more physical parameters of the physical system based on thespatial-temporal emulator model, and utilizing the estimate one or morephysical parameters for at least one of a plurality of applications.

Another embodiment includes a system for generating simulations ofphysical variables of a physical system. The system includes a pluralityof sensors, sensing physical data, one or more computing devicesconnected through a network to the plurality of sensors, and memoryincluding instructions. When the instructions are executed, the one ormore computing devices enable the system to obtain observational data,wherein the observational data includes at least one source of physicaldata sensed by the plurality of sensors, obtain numeric simulation data,and fuse the observation data and the numeric simulation data. Thefusing includes the system operation to preprocess the observationaldata and the numeric simulation data to remove inconsistencies of theobservational data and the numeric simulation data, process thepreprocessed observational data and the numeric simulation data toextract interpretable structures and patterns within that data usingground truth and labeled information to create domain interpretabledata, normalize the preprocessed observation data, the numericsimulation data, and the domain interpretable data layers, and increasea resolution of a gridding of the normalized preprocessed observationdata, numeric simulation data, and domain interpretable data layers. Thesystem further operates to train a spatial-temporal emulator model for aphysical numerical model using the normalized preprocessed observationdata, the numeric simulation data, and the domain interpretable data,incorporate prior knowledge of the physical system into thespatial-temporal emulator model, estimate one or more physicalparameters of the physical system based on the spatial-temporal emulatormodel, and utilize the estimate one or more physical parameters for atleast one of a plurality of applications.

Other aspects and advantages of the described embodiments will becomeapparent from the following detailed description, taken in conjunctionwith the accompanying drawings, illustrating by way of example theprinciples of the described embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of a system for generating simulations ofphysical variables of a physical system, according to an embodiment.

FIG. 2 is a flow chart that includes steps of a method of generatingsimulations of physical variables of a physical system, according to anembodiment.

FIG. 3 is a more detailed block diagram of a system for generatingsimulations of physical variables of a physical system and a subset ofuse cases, according to an embodiment.

FIG. 4 shows underlying functionality of components of aspatial-temporal model, according to an embodiment.

FIG. 5 shows a modular design of a physics-enabled emulator, accordingto an embodiment.

FIG. 6 shows an inference mode of an emulator pipeline forspatial-temporal modeling, according to an embodiment.

FIG. 7 shows a system for training a spatial-temporal model, accordingto an embodiment.

FIG. 8 shows a basic implementation of the emulator model trainingsystem described in FIG. 7 , according to an embodiment.

FIG. 9 shows a process for compressing an emulator model for use on edgeand IoT devices, according to an embodiment.

DETAILED DESCRIPTION

The embodiments described include methods, apparatuses, and systems forgenerating simulations of physical variables of a physical system. Atleast some embodiments include a computational platform for building anddeploying 1) physics-informed, scalable machine learning models ascompute-efficient, cost-effective, modular, data-driven “emulators”(surrogates) to expensive process (partial differential equation,PDE)-based environmental computational fluid dynamic (CFD) simulators ofspatial-temporal processes represented as regular/irregular gridded dataover time, and 2) highly compressed, high-fidelity versions of theseemulators for embedded hardware (field programmable gate arrays (FPGAs),mobile graphic processing units (GPUs)), incorporating data transmissionand power usage constraints into model design. The platform consists ofa general, modular, unified machine-learning-based computationalworkflow, on which different models of different environmental variablescan be developed. This artificial intelligence (AI)-based frameworkassimilates simulation data, observational (remote/ground sensing) data,and explicit physical knowledge (conservation laws, constraints) formodeling realistic spatial-temporal processes. The AI emulatorsdescribed here take full advantage of the machine learning stack,including software frameworks and acceleration hardware, e.g., GPUs ortensor processing units (TPUs). Moreover, they allow 1) much fluster,cost- and energy-efficient inference that is scalable to largegeographical regions, 2) much faster model development, 3) increasedaccuracy of risk estimates via the ability of generating a large numberof scenarios, 4) easy sensitivity and what-if analysis of outputvariables with respect to input observational data, and 5) ability tonatively incorporate a wide variety of real-time observational data.

FIG. 1 shows a block diagram of a system for generating simulations ofphysical variables of a physical system, according to an embodiment. Oneor more sensors 120 sense physical data that can be included withinobservational data. The one or more sensors 120 are connected through anetwork 125 to one or more computing devices 110. The sensed dataincludes, for example, aerial sensors, satellite sensors, cameras,moisture sensors, weather stations, and/or snow stations.

The one or more computing devices 110 operate to obtain theobservational data and to obtain numeric simulation data. The numericsimulation data includes, for example, at least one of weathersimulators, climate simulators, hydrology simulators, computationalfluid dynamics simulators, computer game engines, or other numericalprocess-based simulators.

Further, the one or more computing devices 110 retrieve instructionsfrom memory 112, which when executed cause the computing devices 110 tooperate to fuse the observation data and the numeric simulation data,including preprocessing the observational data and the numericsimulation data to remove inconsistencies of the observational data andthe numeric simulation data, processing the preprocessed observationaldata and the numeric simulation data to extract interpretable structuresand patterns within that data using ground truth and labeled informationto create domain interpretable data, normalizing the preprocessedobservation data, the numeric simulation data, and the domaininterpretable data layers, and increasing a resolution of a gridding(for an embodiment, the gridding includes spatial gridding, for anembodiment, the gridding includes temporal gridding, for an embodiment,the gridding includes spatial and temporal gridding) of the normalizedpreprocessed observation data, numeric simulation data, and domaininterpretable data layers.

For an embodiment, fusing includes a process of bringing all data andother information such as physical constraints to the same computationalworkflow, for example, as afforded by a neural network. For anembodiment, fusing includes a first step of pre-processing input data toremove inconsistencies, a second step of incorporating label data togenerate domain-interpretable structures and patterns (e.g., viasegmentation, classification, or other supervised tasks using labelinformation as supervision) where available, and a third step ofnormalizing the numerical simulation data layers, sensor data layers,and domain-interpretable data layers obtained in second step (that is,bringing these data layers to a common (e.g., tensor-based) formatingestible by downstream models.

For at least some embodiment, removing inconsistencies includes alteringor otherwise eliminating a certain small subset of the data that issuspected of being detrimental to the end goal of the system. For anembodiment, this includes automatically 1) identifying and removing datasamples that are corrupted (e.g., as a result of sensor error), 2)identifying and removing data samples that do not contain enough usefulinformation or signal, 3) identifying and removing data samples that areotherwise less valuable, as indicated by domain-specific metrics.

For at least some embodiment, interpretable structures and patternsinclude spatially or temporally coherent renderings of subsets of theinput or output data used by the platform that can be ascribedscientific or technical meaning of high degree of interest to humandomain experts such as climate scientists, risk modelers, or others. Forat least some embodiments, these interpretable structures and patternsare obtained by 1) training supervised or semi-supervised learningmodels for tasks including segmentation, classification, or regression,where the input of the models comprises at least one of the numericalsimulation data, the observational sensor data, or physical knowledgeand constraints, and the output of the models are the label informationdata, or derivations of the label information data; 2) using thesetrained models to perform inference, whereby the input of the trainedmodels.

For at least some embodiments, ground truth and label informationinclude annotations of spatially or temporally coherent structures(e.g., polygons, pixel masks, etc.) produced by humans, some of whom maybe domain experts in scientific or other topics.

For at least some embodiments, domain interpretable data includes outputdata layers that are produced through a process that utilizes groundtruth and label information to identify domain-interpretable structuresand patterns.

For at least some embodiments, increasing the resolution of griddingincludes a process by which data structured on a coarser grid (i.e., ateven or uneven intervals in space and/or time) is brought to a refinedgrid by applying specific or proprietary interpolation and extrapolationtechniques. For an embodiment, increasing the resolution of griddingincludes 1) pre-processing the coarse-resolution input data, includingnumerical simulation data and observational sensor data; 2) normalizingthe pre-processed coarse-resolution data; 3) applying optimizedinterpolation filters to successively interpolate the normalizedcoarse-resolution to increase the resolution of this data.

Further, the one or more computing devices 110 operate to train aspatial-temporal emulator model for a physical numerical model using thenormalized preprocessed observation data, the numeric simulation data,and the domain interpretable data, and further, incorporate priorknowledge of the physical system into the spatial-temporal emulatormodel. For at least some embodiments, these steps include 1) setting upa model architecture design, e.g., designing the specifics of the layersand connectivity of a neural network, 2) defining an appropriate target(or fit) function that allows the parameters of the model to beoptimized, 3) ingesting the processed data layers produced above intothe model, 4) setting up an appropriate optimizer method for iterativelyupdating the model parameters until a desired level of fitness isachieved.

Further, the one or more computing devices 110 operate to estimate oneor more physical parameters of the physical system based on thespatial-temporal emulator model.

Finally, the estimated one or more physical parameters are utilized forat least one of a plurality of applications 130. The computing devicescan be connected to the applications through the network 125. Theseapplications may include the control of the operation of physicaldevices and apparatuses, including 1) wind power turbines through windintensity risk profile forecasting (that is, physically control the windpower turbines based on the sensed information of the sensors, and thesubsequent processing to generate the estimated one or more physicalparameters; 2) hydro-plants through hydropower generation riskforecasting (that is, physically control select parameters of thehydro-plants); 3) fire first response control instruments and operatingsystems that decide mitigation resource deployment (e.g., where to sendfire trucks or where to drop water from helicopters) based on inferencesabout fire risk.

As will be described, at least some embodiments include the computingdevices 110 retrieving a model (such a spatial-temporal model 114)through the network 125. The spatial-temporal model 114 may be anuntrained model.

FIG. 2 is a flow chart that includes steps of a method of generatingsimulations of physical variables of a physical system, according to anembodiment. A first step 210 includes obtaining observational data,wherein the observational data includes at least one source of physicaldata sensed, including one or more sensors sensing the physical data. Aspreviously stated, the sensed data includes, for example, aerialsensors, satellite sensors, cameras, moisture sensors, weather stations,and/or snow stations. The observational data includes measurement dataobtained from one or more sensors, whereby the mechanism of dataacquisition and generation entails the processing of physical signalsthrough one or more physical apparatus (for example, satellite imagery).A second step 220 includes obtaining numeric simulation data. Aspreviously stated, the numeric simulation data includes, for example, atleast one of weather simulators, climate simulators, hydrologysimulators, computational fluid dynamics simulators, or computer gameengines. For an embodiment, the simulation data includes data generatedby running numerical simulation models, whereby certain a-priorispecified processes are explicitly modeled (for example, via dynamic orstatistical equations), computations are performed that calculate theevolution of these previously-specified processes over space and time,and the resulted numerical calculation data is stored by loggingvariables of interest sampled at certain time and space intervals.

The observation data and the numeric simulation data are fused, whereinfusing includes a third step 230 which includes preprocessing theobservational data and the numeric simulation data to removeinconsistencies of the observational data and the numeric simulationdata, a fourth step 240 which includes processing the preprocessedobservational data and the numeric simulation data to extractinterpretable structures and patterns within that data using groundtruth and labeled information to create domain interpretable data, afifth step 250 which includes normalizing the preprocessed observationdata, the numeric simulation data, and the domain interpretable datalayers, and a sixth step 260 which includes increasing a resolution of agridding of the normalized preprocessed observation data, numericsimulation data, and domain interpretable data layers. A seventh step270 includes training a spatial-temporal emulator model for a physicalnumerical model using the normalized preprocessed observation data, thenumeric simulation data, and the domain interpretable data. An eighthstep 280 includes incorporating prior knowledge of the physical systeminto the spatial-temporal emulator model.

A ninth step 290 includes estimating one or more physical parameters ofthe physical system based on the spatial-temporal emulator model. Atenth step 295 includes utilizing the estimate one or more physicalparameters for at least one of a plurality of applications.

For at least some embodiments, the sensor data includes physicalparameters of atmospheric and/or surface and/or subsurface flows, forexample one or more of the following: wind speed, temperature, pressure,atmospheric particulate matter, precipitation, soil moisture, andothers. For at least some embodiments, physical sensors sense the windspeed, the temperature, the pressure, the atmospheric particulatematter, the precipitation, the soil moisture, and others.

For at least some embodiments, the generated physical parametersinclude, or more of wind speed, temperature, pressure, atmosphericparticulate matter, precipitation, soil moisture, hail, wildfire flamefront, snow water content, snow cover, vegetation cover, vegetationtype, and others.

For at least some embodiments, the generated physical parameters areused across specific use cases, including those listed in FIG. 3 :Renewable Energy generation 341, Agriculture 342, Winter Sports/Resorts343, Insurance and Reinsurance 344, Investments and Hedge Funds 345,Capital Markets 346, Oil & Gas 347, as well as Other Verticals 348.

As previously stated, the observational data and the numeric simulationdata are preprocessed. For an embodiment, the preprocessing of the dataincludes a process by which input data is modified or transformed toremove clear inconsistencies.

As previously stated, the preprocessed observational data and thenumeric simulation data are processed to extract interpretablestructures and patterns within that data using ground truth and labeledinformation to create domain interpretable data. For an embodiment, theground truth includes generally-acceptable standard for the value ofcertain simulated or observed variables, typically acquired viacustom-made sensors or human annotation.

As previously stated, the preprocessed observation data, the numericsimulation data, and the domain interpretable data layers arenormalized. For at least some embodiments, the normalization of the dataincludes a process by which one or multiple input data sources (eitherobservational or simulation, or both) are brought to a common,standardized format that can be easily ingested and acted upon by afurther computational workflow downstream. This may include, but is notlimited to, specific steps to bring one or multiple input data sourcesto the same spatial or temporal resolution, or learn distributed orhierarchical representations of the preprocessed observation orsimulation data such as vector or tensor embeddings in a commonhigh-dimensional space.

For at least some embodiments, the numeric simulation data additionallyincludes reanalysis data obtained from previous data assimilation work,collected from public scientific databases. For an embodiment, thereanalysis data includes data sets obtained by assimilating simulationdata with observational data according to certain statistical andnumerical methods.

For at least some embodiments, the observation data and the numericsimulation data include gridded 2D, 3D, or higher-dimensional data overtime. For at least some embodiments, the data is gridded (2, 3 or moredimensions) over time. For at least some embodiment, the data includesdense spatial information that include either regular grids (e.g., 2Dimages), or irregular grids (e.g., adaptive mesh resolutionsimulations). For at least some embodiments, the observation data andnumeric simulation data include time series data. That is, data pointsare collected over time at specific locations which may be spatiallysparse.

For at least some embodiments, the ground truth and labeled informationdata includes 3^(rd) party data silos, including, but not limited to,ground truth annotations, labeled data, and survey data. This mayinclude, for example, polygon contours or pixel-label masks indicatingcertain atmospheric structures like atmospheric rivers, tropicalcyclones, or hurricanes; land structures such as vegetation type orhealth, snow cover masks; or data on infrastructure such as surveys,asset characteristics, land use characteristics etc.

For at least some embodiments, incorporating prior knowledge of thephysical system into the emulator model includes utilizing statisticalor domain-inspired constraints in the training process of the emulatormodel (“soft” constraints). For an embodiment, this is implemented byincorporating terms into the model optimization loss (fitness) functionpenalizing the departure of model output from physical propertiesexpected for the system studied.

For at least some embodiments, incorporating prior knowledge of thephysical system into the emulator model includes explicitly engineeringat least one component of the emulator model architecture (e.g.,convolutional filters, latent space) to impose structure (“hardconstraints”) that leads to physically-feasible model output. Forexample, in the case when the model is a convolutional neural network,such a constraint may be that certain convolutional filters be fixed torestrict the class of operators they learn to represent numericaldifferentiation.

For at least some embodiments, estimating one or more physicalparameters of the physical system includes atmospheric flow, surfaceflow, hydrology, climate, or weather variables including at least one oftemperature, wind, precipitation, flow speed, or soil moisture.

For at least some embodiments, training the emulator model includes 1)retrieving an untrained model, 2) predicting physical model outputcomprising applying a current model to the normalized preprocessedobservation data, the numeric simulation data, and the domaininterpretable data, 3) testing and adapting the current model byupdating model parameters, and 4) iterating on this process until ameasure of convergence is achieved.

For at least some embodiments, testing the current model comprises 1)measuring a fit of the predicted physical model output and 2) computingan error function by comparing the measured fit with physical knowledge.

For at least some embodiments, retrieving a model (such aspatial-temporal model 114 of FIG. 1 ) includes at least one of thefollowing processes, as for example: 1) designing the model architectureto reflect domain knowledge and current best practices, theninitializing the architecture with an initial value of the parameters,following best practices and state-of-the art procedures; 2) obtainingan existing model architecture with already-trained parameters, andmodifying only part of the architecture, e.g., initializing trainableparameters to be only a subset of all model parameters.

At least some embodiments further include building a plurality ofemulator models for different use cases including at least one ofhydroclimate models, climate models, numerical weather predictionmodels, meso-scale weather models, surface flow models, subsurface flowmodels, environmental fluid dynamics models. At least some embodimentsfurther include retrieving an initial model for each of the differentuse cases and building a corresponding trained model of one or more ofthe use cases.

At least some embodiments include building emulator models for one ormultiple use cases by connecting special-purpose components (alsoreferred to here as modules or operators), each special-purposecomponent performing specific tasks, including, but not limited to, 1)super-resolution in space (implementing the functionality of increasingthe resolution of gridded data over space), 2) super-resolution in time(implementing the functionality of increasing the resolution of griddeddata over time), 3) domain knowledge incorporation (implementing thefunctionality of incorporating domain knowledge or physical constraintsor laws), and 4) spatial-temporal modeling (implementing thefunctionality of spatial-temporal emulation). At least some embodimentsinclude connecting all or a subset of the special-purpose componentsinto custom computational pipelines specific to the needs andrequirements of one or more applications. At least some embodimentsinclude generating new emulator models by connecting either individualmodules or other emulator models in patterns required by thespecifications of different applications at hand.

At least some embodiments include generating one or more of the emulatormodels or modules/operators, and connecting them (in that, the output ofone or more models become the input of other models) in ways specifiedby the application of interest, generating new, derived emulator models.For at least some embodiments, connecting emulators includes 1)formatting the output or one or more emulators in the form of tensors;2) concatenating these output tensors in specific ways required by theinput requirements of the other emulators that will ingest this output;3) set up another set of emulators to ingest this concatenated outputtensor data as inputs. For example, as indicated in FIG. 3 , a wildfireemulator model may be comprised of a wind emulator module, of atemperature emulator module, and of a hydroclimate emulator module assub-components. The output of such emulator modules will be in the formof tensors (gridded data), either 1D, 2D, or in more dimensions,ingestible as input by other emulator modules.

At least some embodiments include generating one or more emulator modelsthat are compressed versions of initial emulator models, yet retainingthe performance of the initial models. For at least in some embodiments,this is achieved through eliminating model parameters or aspects ofmodel architecture that have minimal impact of the model performance. Atleast some embodiments include compressing one or more emulators by 1)reducing emulator size (as measured by number of parameters or memoryfootprint) or 2) reducing the complexity of data transformationsperformed by the emulator, with minimal impact on emulator operationalperformance. For example, for at least some embodiments where theemulator module is implemented as a neural network architecture, thisincludes 1) reducing the number of layers, 2) reducing the number ofneurons, or 3) reducing the number of parameters specifying thecomputational behavior of each neuron, and 4) removing connectionsbetween existing layers or neurons in the model by systematicallypruning sub-networks (also called pathways) through the neural networksthat do not contribute substantially to model output, as measured by thechange in performance when those pathways are removed.

At least some embodiments include pruning pathways through neuralnetworks. For at least some embodiments, pruning the pathways throughneural networks includes 1) retrieving a trained emulator model, wherebythe model architecture is based on a neural network, 2) selectingsub-networks as candidates for removal (either at random, or byemploying best-practices in the literature), 3) removing the candidatesub-networks from the emulator model and re-training the model, 4)computing model performance on a validation dataset and standard,predefined task, and 5) iterating until a model is achieved that has asize below a certain pre-defined level, as well as maintaining the samelevel of performance on the validation dataset as the original emulatormodel.

For at least some embodiments, the spatial-temporal emulators includemachine learning-based models and computational workflows thatapproximate (e.g., in a statistical or dynamical sense) the behavior ofnumerical, process-based models. Here, machine learning (ML) andartificial intelligence (AI) are referred to interchangeably.

FIG. 3 is a more detailed block diagram of a system for generatingsimulations of physical variables of a physical system, according to anembodiment. At least some of the described embodiments include anartificial intelligence (AI) platform for data fusion and AI-basedphysical system emulation. The described embodiments allow for quicklydeveloping modular, ultra-fast, scalable deep learning physical emulatormodels that run on a wide variety of machine learning hardware andnatively use contemporary open-source machine learning softwareframeworks. The described embodiments integrate physical simulation data(including, but not limited to, data on climate and weather systems andbuilt infrastructures) with empirical observation data fromremote-sensing (RS), local ground sensor (e.g., weather station), andlabel (ground truth) data from surveys and data silos.

FIG. 3 illustrates the concepts of at least some embodiments of an AIplatform for physics-enabled AI emulation of physical systems, focusingon components of the computational flow and application layer. As shown,inputs to spatial-temporal model(s) 312 includes numerical simulationdata 321 (output from numerical simulation models). For an embodiment,this data is formatted as dense grids (either regular or irregular) overtime.

Further, as shown, inputs to the spatial-temporal model(s) 312 includesobservational remote-sensing data 322 (acquired, for example, byphysical sensors on satellites). For an embodiment, this data isformatted as dense regular grids over time.

Further, inputs to the spatial-temporal model(s) 312 may includereanalysis data (combining numerical model output with observationaldata, obtained from publicly-available databases or from non-publicdatabases). This data typically is formatted as dense regular grids overtime.

Further, as shown, inputs to the spatial-temporal model(s) 312 includesobservational ground-sensing data 323 (from IoT sensors such as weatherstations or cameras), available from public databases or throughnon-public sources. These data are typically geographically-sparse, andformatted as time-series.

Further, as shown, inputs to the spatial-temporal model(s) 312 includesdomain knowledge and physical constraints 324. This information may beembedded at different points in the computational pipeline, for examplein the machine learning model architecture itself (e.g., in the designof the neural network layers or connection patterns between layers,i.e., hard constraints), or in the training procedure of the machinelearning model (e.g., via the inclusion of domain-inspired penalty termsin the loss function of the optimization).

Further, as shown, inputs to the spatial-temporal model(s) 312 includeslabels, metadata, and ground-truth data 325 (available from publicsurveys and data siloes/databases, or obtained via custom labeling).This data is typically in the form of metadata and relational data,e.g., polygons or pixel-level class labels for semantic segmentation ofimages).

For at least some embodiments, the spatial-temporal model(s) 312implements a unified, modular machine-learning computational workflowfor building physics-informed machine learning emulators (surrogatemodels) of physical systems. The platform allows for both building theemulators (the training phase) and for running the emulators (aninference phase).

For at least some embodiments, the spatial-temporal model(s) 312operating on one or more servers 310 provides for building of emulatorsusing the same underlying modular computational workflow. Supportedareas include, but are not limited to, environmental computational fluiddynamics-based applications such as hydroclimate models (emulator 313),climate models (emulator 314), numerical weather prediction models(317), geophysical subsurface flow models (emulator 315), or otherphysical and process-based simulator models 316. The emulator modulesare themselves built by composing basic AI modules fulfilling certainspecialized functions, also called operators.

The outputs of one or more emulators can be used either standalone,e.g., the hydroclimate emulator model outputs mountain snowpack snowwater equivalent grids 333. In addition, the outputs of some emulatormodels can be used as inputs to other emulator models, e.g., theWeather/Meteorology emulator model 317 outputs, for example, Wind Grids361, Precipitation Grids 363, and Temperature Grids 365, which can beused, in addition to the outputs of other emulator models such as theclimate emulator model 314, as inputs to building a Wildfire EmulatorModel 318.

At least some embodiments include building complex emulator models bycomposing emulators in a modular, “plug-and-play” fashion. As eachmodule is built following the same computational workflow, composing oneor several emulators yields a new emulator, itself composable with otheremulators or basic modules. For an embodiment, the system furtheroperates to generate complex emulator models comprising connecting aplurality of the emulator models, wherein the emulator models are builtby connection sub-modules that perform specific functions includingincreasing spatial resolution and temporal resolution of gridded data.That is, for an embodiment, the emulator models are built by connectingsub-modules performing specific functions, including, for example, forincreasing the spatial resolution of gridded data, for increasing thetemporal resolution of gridded data, for spatial-temporal modeling.

At least some embodiments include implementation of a flexibleApplication Programming Interfaces (APIs) and User Interfaces (UIs)layer designed for exposing platform capability for use-cases ofinterest, including, but not limited to, sectors such as renewableenergy, agriculture, insurance, reinsurance, real estate, winter sportsresorts, investment and hedge funds, capital markets, or oil & gas.

FIG. 4 shows components of a spatial-temporal model, according to anembodiment. At least some embodiments utilize generative machinelearning models such as Generative Adversarial Networks (GANs),Variational Autoencoders (VAEs), or Neural Ordinary DifferentialEquations (NeuralODEs). For at least some embodiments, training aspatial-temporal emulator model for a physical numerical model using thenormalized preprocessed observation data, the numeric simulation data,and the domain interpretable data can be implemented using the machinelearning framework illustrated in FIG. 4 .

Specifically, at least some embodiments utilize GANs for domain/modalitytranslation that allow implicit sampling in forward and inverse domains.For example, in a hydroclimate and snow water equivalent estimationemulator, one domain is that of the input variables (e.g.,meteorological, topographic, and other inputs), while the other is thatof the output variable of interest, snow water equivalent.

As shown in FIG. 4 , at least some embodiments implement model emulationas learning mapping functions between two or more domains. Depicted inFIG. 4 are two domains X (e.g., input meteorological variables in someembodiments) and Y (e.g., hydroclimate model output, snowpack water insome embodiments). The process for learning the mapping functionsconsists of the following steps: 1) training a model that learns aforward mapping function G making the translation between domains X andY, in the 2-domain example; 2) training a model to learn an inversemapping F between domains Y and X, whereby both forward and inverse mapsG and F between X and Y are learned in one training loop.

At least in some embodiments, this framework explicitly structures thelatent spaces of G and F to allow control over the source of randomnessZ, as shown schematically in FIG. 4 . This allows the drawing of diversesamples from the underlying data manifolds in X/Y. These ensembles ofscenarios can then be used in a Monte Carlo setting to compute empiricalCDFs and other risk measures of variables of interest.

At least in some embodiments, this framework may be used for performingAI-based data assimilation, including building emulator modules (oroperators) for modality translation between simulation variables ofinterest (typically not observable directly, e.g., surfaceprecipitation) and (proxy) observational data (e.g., top of theatmosphere water column remote sensing radar). These modalitytranslation modules have the same computational workflow (inputs andoutputs are tensors, i.e., gridded data) and can be connected intolarger pipelines.

As previously stated, for at least some embodiments, GANs or othergenerative learning models such as VAEs or NeuralODEs are utilized forspatial-temporal modeling. At least some embodiments of the systemleverage machine learning approaches to model the temporal evolution ofdynamical systems. At least some embodiments leverage architectures thatmodel temporal dynamics of physical processes to generate predictionsand scenarios of the future evolution of the processes modeled. At leastsome embodiments leverage machine learning architectures for modelingtemporal correlations, including Recurrent Neural Networks (RNNs),including variants such as Long Short-Term Memory Networks (LSTMS). Atleast some embodiments leverage attention models such as Transformers tomodel long-range serial correlations. At least some embodiments leverageconvolutional neural networks (CNNs) to model spatial correlations. Asthe embodiment in FIG. 4 shows, Transformers model temporal correlationsby learning how to weigh the most relevant parts of the past whilegenerating predictions of the future evolution of the process modeled,thereby capturing the different temporal correlation scales of systemsof interest.

As previously stated, at least some of the spatial-temporal modelsincorporate domain knowledge, either as hard or soft constraints, asdiscussed above. This previous research on Physics-Informed GANs(PI-GANs) for fluid dynamics, land use, or hydroclimate model emulationboth show the effectiveness of incorporating physics or domain-inspiredconstraints into machine learning architectures (e.g., cannot have snowon water areas, higher water content for higher elevations, etc.). Thisresearch has shown that incorporating such knowledge achieves certainbenefits, including reducing the optimization search space, leading tofaster convergence to better, physically-feasible solutions (FIG. 4-3 ).At least some of the spatial-temporal models that utilize generativemachine learning model designs such as GANs, VAEs, or NeuralODEs,incorporate a) soft constraints (via penalty terms in the lossfunction), and b) hard constraints, via explicit structuring of the MLmodel latent space and convolution neural network (CNN) filters (seen asspecialized differential operators).

At least some embodiments leverage deep learning-based super-resolutionmethods for numerical simulation and remote-sensing data to implementemulator modules for increasing the spatial or temporal resolution oftensors (gridded data). At least some embodiments include creating highresolution climate data grids by downscaling baseline climate layersusing CNN-based image super-resolution algorithms. The relevantobservational data products (satellite and ground station) on keyparameters relevant to applications of interest, for examplehydroclimate modeling (i.e., precipitation, radiation, temperature,surface radiation, and wind). At least some embodiments implementsuper-resolution methods based on generative learning techniques such asGANs, VAEs, or NeuralODEs,

In addition, at least some of the described embodiments implement custommethods and pipelines for quantifying and propagating uncertainty ingridded simulation and remote-sensing imagery data over time, includingBayesian learning techniques for deep neural networks to model errorpropagation.

At least some of the described embodiments implement techniques toenhance training stability, convergence, and scaling of deep generativelearning models. Specifically, in at least some embodiments, this isimplemented by 1) designing custom architecture components (e.g., customlayers if the emulator module is a neural network), 2) designing theoptimization loss functions to include regularizing loss terms, or 3)designing neural network layers and connections between them thatexpress neural networks as systems of coupled ODEs/PDEs, allowing to usemature results from linear dynamical systems analysis to enhancetraining stability.

At least some embodiments include mechanisms to allow fine-grainedcontrollability of the conditional generative architectures and explicitstructuring of the latent space to relate it to interpretable physicalsimulation parameters. These mechanisms are implanted by 1)incorporating features and latent noise at different stages in thenetwork architecture, 2) imposing assumptions about the distributionsfollowed by the latent space random vectors, 3) incorporating boundaryand initial conditions such as complex geometries/terrain elevation andremote-sensing observations to enable fast, realistic interpolation insimulation parameter configuration space.

At least some embodiments include architectural designs for implementingscenario analysis and hypothesis generation. This capability isimplemented by 1) conditioning forecasts and scenario simulations onreal-time observational data (i.e., remote- and ground IoT sensing), 2)implementing architectures for real-time updates of error propagation,3) implicitly sampling the potentially unknown distributions of thevariables in the physical system modeled, and 4) using the scenariogeneration capability for Monte-Carlo simulations and estimatingunderlying errors, empirical distribution curves, hazard curves,financial loss curves, exceedance probability curves, at least in someembodiments.

At least some embodiments leverage the end-to-end differentiability ofdeep learning architectures, and gradient computations viabackpropagation for emulator model or module estimation to allowestimating both gradients and standard errors of emulator parameters.This allows computing the sensitivity of outputs (e.g., in the case ofsnow water equivalent upstream of a hydropower plant) with respect tocertain inputs of interest (e.g., temperature or wind speed measured atthe location of a ground station) for what-if analyses.

At least some embodiments include a modular emulator design using aunified computational workflow based on gridded (tensor) data being bothinput as well as output of emulator modules or operators. At least someof the described embodiments leverage a machine-learning centriccomputational workflow, i.e., the patterns in transformations of inputand output data to different components of the processing pipeline. Atleast some of the described embodiments implement several standard datainterchange formats, including tensors (1D vectors and 2D grids, orhigher-dimensional) over time, including both raw data as well asembeddings learned from the data via representation learning models(e.g., autoencoders). Inputs and outputs to the modules illustrated inFIG. 5 include tensors, which allows for an efficient data flow and forinterchangeable connection patterns between modules.

At least some embodiments include compressing the machine learningemulators while incorporating constraints imposed by the computingdevices the emulators are run. For example, this may include edgecomputing devices, which may pose memory, data bandwidth, or powerconsumption constraints. For certain applications of interest, the AIemulator models need to run efficiently on local devices to generateaccurate hypotheses on system evolution in real-time and in situationswith limited availability of wireless/cloud connectivity (e.g., celltowers fail during a wildfire). For this, at least some of the describedembodiments leverage state-of-the-art machine learning research tocompress the machine learning model to fit on the available edgehardware in terms of memory and CPU/GPU resources, under constraints onthe target device energy use and user latency, while retaining highfidelity to the uncompressed model, using distillation and ML modelcompression techniques that have shown 100× model size reduction on edgedevices.

It is beneficial to deploy and run emulator models on edge devices toallow certain applications that require in-the-field operability andreal-time computation under severe data bandwidth, power usage, andother constraints, such as fire spread prediction for firefirst-responders, during and after-the-event claims adjusting forinsurance companies, or low-latency computations for high-frequencytrading.

An embodiment includes compressing an emulator model for use on edge andIoT devices. For an embodiment, this includes compressing the trainedspatial-temporal emulator model, including 1) generating candidatemutations of an architecture of the trained spatial-temporal emulatormodel to reduce a number of parameters or connections of the parameters;2) evaluating a performance of each of the candidate mutations of thearchitecture on validation data using metrics; 3) retaining a subset ofcandidate mutations exhibiting best performance on the metrics; 4)iterating steps 1-3 until convergence to a desired reduction in size ofthe trained spatial-temporal emulator model, yielding a compressedtrained spatial-temporal model (that is, the steps of generatingcandidate mutations, evaluating the performance of the mutations, andretaining a subset of the candidate mutations is repeated over and overuntil a number of parameters or connections of parameters has beenreduced which reduces the size of the trained spatial-temporal emulatormodel to a desired size); 5) deploying the compressed trainedspatial-temporal emulator model to one or more edge devices. The reducedsize makes the compressed trained spatial-temporal emulator modeloperable on edge devices, such as, a mobile device, such as, a mobilesmart phone.

For at least some embodiments, compressing the machine learningemulators so that the machine learning emulators (such as,spatial-temporal emulator models) are operable on processing-limitededge devices includes A) generating mutations of the initial networkarchitecture by 1) replacing some of the large (e.g., 3×3) convolutionalfilters with smaller filters (e.g., 1×1); 2) decreasing the number ofinput channels to the large filters; and 3) moving some down-samplingoperations to later stages in the network, and 4) computing modelaccuracy on a holdout sample; B) iterating in an evolutionary fashion,keeping the most promising network mutations from among the populationgenerated, and feeding them to step A.

FIG. 5 shows the modular design of a physics-enabled emulator such asshown in FIG. 1 as spatial-temporal model 114 according to anembodiment. As previously described, the spatial-temporal model 114 canbe retrieved for a variety of applications, such as a wildfire spreadforecasting application, a snow water equivalent estimation application,etc. The embodiment of FIG. 5 provides an example of an architecture ofa physics-informed AI emulator (such as the spatial-temporal model 114).It illustrates one possible configuration among many, suited forspatial-temporal modeling of computational environmental fluiddynamics-based numeric models (e.g., climate, hydroclimate, meteorology,weather etc.). The inputs include Input physical constraints and domainknowledge 512, including both quantitative relationships and physicallaws, and qualitative relationships (e.g., the change in one quantityleads to a change in another), Input observational data, reanalysis data514, and numerical simulation data, typically grids over time, and Inputlabel data 516 (e.g., class labels for image pixels, annotations,surveys etc.).

The output 527 includes a sequence of gridded data over time, wherebyeach cell of the grid contains a measure of risk (probabilitydistribution) of the output parameter(s).

For at least some embodiments, a data normalization and embedding(representation) learning module 521 operates for receiving sensor data,and extracting representations of numerical simulation data,observational data, or reanalysis data. For at least some embodiments,extracting representations from input data includes 1) retrieving apreviously-trained or untrained model; 2) ingesting the data through themodel; 3) setting up the model to perform specific tasks, for exampleunsupervised data reconstruction, supervised or semi-supervisedsegmentation or classification.

A label data integration module 522, which may be embodied as, forexample, via modules for 1) gridded data segmentation, 2) gridded dataclassification, and 3) object/structure detection in gridded data. Thismodule implements functionality for processing imagery/gridded data overtime and extract domain-specific, interpretable structure andinformation. These functions may also include representational learningfor data other than gridded (tensor) formats, including, for example,embedding/representation learning for tabular/relational data thatnormalize this data to vectorial embedding representation for easyintegration into deep learning pipelines.

A downscaling (super-resolution) modules 523 operate to grid data overtime. This may be a spatial downscaling module, or a temporaldownscaling module, or a spatial-temporal downscaling module.

A spatial-temporal modeling emulation module 524 operates to modelspatial and temporal correlations.

An ensemble and scenario generation and simulation module 525 operatesto create summaries of the distributions over space and time of theoutput variables of interest (e.g., wildfire spread, snow waterequivalent, wind fields) that are useful for informing one or moreapplications. It operates by 1) accessing the output of thespatial-temporal modeling module 524 to generate a multitude Monte Carloscenarios and ensembles with different initialization configurations forthe simulated process; 2) computing statistics (such as histograms,probability distribution functions, cumulative distribution functions,etc.) for each point in space and time in the domain considered, basedon aggregating the ensembles and scenarios generated; and 3) validatingthese statistics against key metrics implemented in the MetricComputation and Validation Module 526.

A validation module 526 implements computation of spatial and temporalperformance metrics as appropriate for the application at hand. For anembodiment, the computation of spatial and temporal performance metricsincludes: 1) two-point correlation spectra, 2) temporal coherence, 3)autocorrelation functions, or 4) histograms of values.

At least some embodiments of scenario generation use the samplingcapabilities of the spatial-temporal modeling module 524 to generatescenarios and hypotheses, representing ensembles of models. For at leastsome embodiments, these ensembles of models are then used to derivemeasures of risk of the spatial and temporal evolution of the physicalparameters modeled, i.e., statistics (such as cumulative distributionfunctions) derived empirically.

At least some embodiments include other AI modules performing specificfunctions, with a similar input/output data flow as the modulesdescribed above, connected to each other in specific ways.

At least some embodiments include AI emulators that are interoperableand constructible through specific application programming interfaces(APIs). These APIs are accessible either as libraries or modules inprogramming languages (e.g., Python, C/C++, or Julia), or via a webinterface (e.g., REST). The API layer allows relevant functionality tobe exposed to software developers that would design wrappers, graphicaluser interfaces, and access patterns for different business use casesand products. New functionality is achieved by programmaticallyconnecting one or more of the emulator modules or operators in waysspecific to the application of interest. For example, one applicationmay require spatial-temporal emulation, spatial downscaling, andscenario generation functionality, whereas another application mayinstead require temporal downscaling and scenario generationfunctionality.

For at least some embodiments, the example emulator, the emulatoroperators (or modules) as described, and other emulators built using thedescribed embodiments are part of a library of AI emulator modules thatconstitute a “plug-and-play” ecosystem of modules that are accessiblethrough the APIs.

At least some embodiments include building new emulators according tothe following process: 1) the user constructs specific API callsinstantiating a selection of existing modules from the library; 2)connectivity patterns between instantiated modules are specifiedaccording to the task at hand (e.g., wind emulation, precipitationemulation and downscaling, etc.), whereby the output of some modulesserves as input to other modules; 3) the new emulator module is verifiedand registered as valid module, and committed to the library, therebybecoming available for further re-use. As such, the describedembodiments enable the creation of specific “apps” that require limitedknowledge of underlying physical systems that are emulated or of theunderlying machine learning and high-performance computing technologies.

FIG. 6 shows an inference mode of an emulator pipeline forspatial-temporal modeling, according to an embodiment. At least someembodiments include inference with physics-informed AI emulators 620(such as, the previously described spatial-temporal emulator model,i.e., a trained version of the untrained spatial-temporal model 114). AsFIG. 6 illustrates, at inference time (when the AI emulator 620 is usedto generate predictions and scenarios), the only data the emulator modeluses as input is observational data 610, if available. In inferencemode, the AI emulator does not require any simulation data or beingotherwise connected to a numerical simulation infrastructure orcodebase.

Further, as shown in FIG. 6 , an output includes a gridded distributiondata over time, whereas in each cell of the grid a measure of theprobability distribution of the output variable(s) is computed.

FIG. 7 shows training of a spatial-temporal model, according to anembodiment. At least some embodiments include training physics-informedAI emulators. FIG. 7 shows the data products, either sources orgenerated (dashed line, round corner boxes) and the computationalmodules (solid line, square boxes).

Observational sensor data 745 includes one or more variables in thefields of climate, weather, energy, hydroclimate, hydrology, subsurfaceflow, or surface flow. This includes processed or derived quantities(e.g., via segmentation and classification) such as obtained from labeldata integration module 522 in FIG. 5 .

A numerical simulator model 730 (optional) includes, but not limited to,computational fluid dynamics (CFD) simulators, global climatesimulators, meso-climate simulators such as for wind, temperature, andpressure, hydroclimate simulators such as for precipitation, or snowwater equivalent.

Physical parameters 710 (optional) includes variables with well-definedphysical meaning and interpretable to human scientists and analysts,which control the output of numerical simulation. These parameters maybe either omitted from being included in the training loop, or includedeither 1) statically, in the sense that their values will not be changedas training progresses, but they are treated as other input data thatthe AI emulator model is conditional on, or 2) dynamically, in the sensethat their value changes during training, via being controlled by theRL/AL Parameter space exploration module 750, based on reinforcementlearning (RL) or active learning (AL) techniques. Pre-generating largequantities of numerical simulation data to cover a wide variety ofinitial conditions for training a spatial-temporal emulator model 114can be extremely compute-intensive and impractical. The RL/AL module 750implements control strategies for closed-loop numerical simulation andmachine learning training, including 1) generating an initial estimateof appropriate values of physical parameters 710; 2) actuating thenumerical simulator 730 to generate a initial quantity of numericalsimulation data 735 corresponding to these initial parameters; 3)computing a measure of generalizability of the model (e.g., performanceunder a validation dataset) for the current configuration of thephysical parameters 710; 4) computing updated estimates of the physicalparameters 710 that are most likely to improve the measure ofgeneralizability, and 5) iterating until a measure of convergence isachieved, e.g., a validation-set performance as measured by the metricsimplemented in Metric Computation and Validation Module 526.

Simulation data 735 is produced by the numerical simulator, eitheron-line as part of the learning loop, or off-line, retrieved fromexisting databases, and serving as one of the inputs to the AI emulator.

An AI emulator module 740 includes, in at least some embodiments, agenerative machine/deep learning-based architecture that is able toaccept tensor-formatted inputs such as, for example, structured regularor irregular grids over time, vector embeddings etc., and outputsstructured grids over time, all in the same data structure format oftensors. An example thereof is given in FIG. 5 . Note that the emulatormodule can be composed of one or more of the ML modules described inFIG. 5 .

A latent space controller 760 implements control mechanisms forstructuring and manipulating the representation (latent) space of thegenerative model implementing the AI emulator to allow for bothcontrollable random sampling as well as conditional generation based oninput data such as physical parameters or observational data.

Synthetic data 755 includes data generated by sampling from thegenerative machine learning model implementing the AI emulator.

An AI emulator inverter 720 (optional) module learns the inverse mappingfrom observational data to interpretable physical parameters of theemulated system that could plausibly give rise to the observed data. Forat least some embodiments, the AI emulator inverter module isimplemented using the domain translation framework described in FIG. 4-1(referred to by the inverse mapping function F in FIG. 4 ).

For an embodiment, the Physical parameter exploratory module 750(optional) implements sampling strategy in physical parameter spacebased on search techniques from reinforcement learning/active learning.Only to be included when the numerical simulator is included as part ofthe same learning loop. For at least some embodiments, the module 750 1)constructs approximate joint distributions of the physical modelparameters; 2) computes a measure of fit indicating how well the AIemulator is able to sample (or interpolate) in a certain region of thephysical model parameter space; 3) propose a number of different samplesof physical model parameters that are likely to maximally increase themeasure of fit; 4) update the current model parameters to theconfiguration that corresponds to the optimal value of the fit measure.

Domain-specific constraints 770 including constraints and physical lawsapplicable to the specific domain being emulated, stemming from physicaltheory or domain expertise. For example, divergence-free flows forincompressible fluids or rotational invariance are typical constraintsincorporated in fluid-dynamics based emulators. The incorporation ofadditional domain knowledge in the form of constraints and physical lawsmay reduce the amount of training data or training time necessary toachieve a desired level of performance.

FIG. 8 shows a simplified implementation of the emulator model trainingsystem described in FIG. 7 , according to an embodiment. This baselinetraining system allows for the creation of physics-informed AIemulators, yet it may require a larger amount of numerical simulationdata 735 to be pre-generated across a wide range of values of thephysical parameters 710. The system in FIG. 7 overcomes this limitationby implementing a closed-loop numerical simulation and machine learningemulator training with explicit strategies for sampling appropriatevalues of the physical parameters 710.

FIG. 9 shows a process for compressing an emulator model for use on edgeand IoT devices, according to an embodiment. Specifically, FIG. 9 showsthe workflow for compressing a trained AI emulator for certainapplications that require running such models locally or in alow-bandwidth or low-power environment. The inputs include an MLemulator model 910 trained as previously described, and hardwareconstraints 950. Compressing the trained AI emulator model 910, for atleast some embodiments, includes a Knowledge and ML Model Compressionsystem 920 that performs the process of: A) generating mutations of theinitial network architecture by 1) replacing some of the large (e.g.,3×3) convolutional filters with smaller filters (e.g., 1×1); 2)decreasing the number of input channels to the large filters; and 3)moving some down-sampling operations to later stages in the network, and4) computing model accuracy on a holdout sample; B) incorporatingHardware Constraints 950 either as soft constraints (penalty terms) intothe optimization loss function or as hard constraints into the design ofthe architecture as in Step A); and C) iterating in an evolutionaryfashion, keeping the most promising network mutations from among thepopulation generated, and feeding them to step A. The resultingcompressed ML Emulator 930 can be deployed and operated on IoT or EdgeDevices 940.

Although specific embodiments have been described and illustrated, theembodiments are not to be limited to the specific forms or arrangementsof parts so described and illustrated. The described embodiments are toonly be limited by the claims.

What is claimed:
 1. A method for generating simulations of physical variables of a physical system, comprising: obtaining observational data, wherein the observational data includes at least one source of physical data, comprising one or more sensors sensing the physical data; obtaining, by one or more computing devices or storage devices that are connected through one or more networks, numeric simulation data; fusing, by one or more computing devices, the observation data and the numeric simulation data, comprising: preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data; processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data; normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers; increasing a resolution of a gridding of the normalized preprocessed observation data, numeric simulation data, and domain interpretable data layers; training a spatial-temporal emulator model for a physical numerical model using the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data; incorporating prior knowledge of the physical system into the spatial-temporal emulator model; the method further comprising estimating, by one or more computing devices, one or more physical parameters of the physical system based on the trained spatial-temporal emulator model; compressing the trained spatial-temporal emulator model, comprising: 1) generating candidate mutations of an architecture of the trained spatial-temporal emulator model to reduce a number of parameters or connections of the parameters; 2) evaluating a performance of each of the candidate mutations of the architecture on validation data using metrics; 3) retaining a subset of candidate mutations exhibiting best performance on the metrics; and 4) iterating steps 1-3 until convergence to a desired reduction in size of the trained spatial-temporal emulator model, yielding a compressed trained spatial-temporal model; and the method further comprising utilizing, by one or more computing devices, the estimated one or more physical parameters for at least one of a plurality of applications.
 2. The method of claim 1, further comprising deploying the compressed trained spatial-temporal emulator model to one or more devices.
 3. The method of claim 1, wherein the numeric simulation data additionally includes reanalysis data obtained from previous data assimilation work or collected from public scientific databases.
 4. The method of claim 1, wherein the observation data and numeric simulation data include time series data.
 5. The method of claim 1, wherein the ground truth and labeled information data includes 3^(rd) party data silos including ground truth annotations, labeled data, and survey data.
 6. The method of claim 1, wherein incorporating prior knowledge of the physical system into the spatial-temporal emulator model comprises utilizing statistical or domain-inspired constraints in the training process of the spatial-temporal emulator model.
 7. The method of claim 1, wherein incorporating prior knowledge of the physical system into the spatial-temporal emulator model comprises engineering at least one component of the emulator model architecture to impose structure that yields a physically-feasible model output.
 8. The method of claim 1, wherein the estimating one or more physical parameters of the physical system includes estimating atmospheric flow, surface flow, hydrology, climate, or weather variables including at least one of temperature, wind, precipitation, flow speed, or soil moisture.
 9. The method of claim 1, wherein training the spatial-temporal emulator model comprises retrieving an untrained model, predicting physical model output comprising applying a current model to the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, testing and adapting the current model.
 10. The method of claim 9, wherein testing the current model comprises measuring a fit of the predicted physical model output, computing an error function by comparing the measured fit with physical knowledge, or adapting the current model based on the computed error function.
 11. The method of claim 1, further comprising building a plurality of emulator models for different use cases including at least one of hydroclimate models, climate models, numerical weather prediction models, meso-scale weather models, surface flow models, subsurface flow models, or environmental fluid dynamics models.
 12. The method of claim 11, further comprising retrieving an initial model for each of the different use cases and building a corresponding trained model of one or more of the different use cases.
 13. The method of claim 11, further comprising generating complex emulator models comprising combining at least two of the plurality of the emulator models.
 14. A system for generating simulations of physical variables of a physical system, comprising: a plurality of sensors, sensing physical data; one or more computing devices connected through a network to the plurality of sensors; memory including instructions that, when executed by the one or more computing devices, enables the system to: obtain observational data, wherein the observational data includes at least one source of physical data sensed by the plurality of sensors; obtain numeric simulation data; fuse the observation data and the numeric simulation data, comprising: preprocess the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data; process the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data; normalize the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers; increase a resolution of a gridding of the normalized preprocessed observation data, numeric simulation data, and domain interpretable data layers; train a spatial-temporal emulator model for a physical numerical model using the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data; incorporate prior knowledge of the physical system into the spatial-temporal emulator model; wherein the system is further enabled to: estimate one or more physical parameters of the physical system based on the spatial-temporal emulator model; compress the trained spatial-temporal emulator model, comprising: 1) generating candidate mutations of an architecture of the trained spatial-temporal emulator model to reduce a number of parameters or connections of the parameters; 2) evaluating a performance of each of the candidate mutations of the architecture on validation data using metrics; 3) retaining a subset of candidate mutations exhibiting best performance on the metrics; 4) iterating steps 1-3 until convergence to a desired reduction in size of the trained spatial-temporal emulator model, yielding a compressed trained spatial-temporal model; the system further enabled to utilize the estimated one or more physical parameters for at least one of a plurality of applications.
 15. The system of claim 14, wherein the compressed trained spatial-temporal emulator model is deployed to one or more devices.
 16. The system of claim 14, wherein training the spatial-temporal emulator model comprises the system operating to retrieve an untrained model, predicting physical model output comprising applying a current model to the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, testing and adapting the current model.
 17. The system of claim 16, wherein testing the current model comprises measuring a fit of the predicted physical model output, computing an error function by comparing the measured fit with physical knowledge, or adapting the current model based on the computed error function.
 18. The system of claim 14, wherein the system further operates to build a plurality of emulator models for different use cases including at least one of hydroclimate models, climate models, numerical weather prediction models, meso-scale weather models, surface flow models, subsurface flow models, or environmental fluid dynamics models.
 19. The system of claim 18, wherein the system further operates to retrieve an initial model for each of the different use cases and building a corresponding trained model of one or more of the different use cases. 